Prepare for Einstein Recommendations
Learning Objectives
After completing this unit, you’ll be able to:
- Define key Einstein Recommendations terms.
- Identify the implementation steps.
Create Personalized Recommendations
Einstein is not only a brilliant scientist and marketer, he is also a magician. And he is ready to reveal his secrets to creating personalized recommendations. In this module, we focus on Einstein Email and Web Recommendations. Using these tools, you can create tailored email, mobile, and web content based on customer behavior and interests. Which is the magic ingredient behind creating personalized content that makes customers happy.
Preparation Is Key
Magic is great when done well, but it requires preparation. Similarly, before you can start sending out magical recommendations, there is some prep work that needs to be done. Let’s start by reviewing the A to Z’s of terms used in Einstein Recommendations.
Term |
What It Means |
What You Should Know |
|---|---|---|
Anonymous Users (versus Known Users) |
A user is labeled anonymous until they become known through an identifier, such as an email address. |
Identification typically occurs when a customer logs in to your website or makes a purchase. |
|
Attributes or Tags
|
Attributes are actions or content that is tracked and used to make recommendations. |
There are standard and required attributes, but you can also create custom attributes (brand, color, category). |
|
Catalog
|
Catalogs store all the metadata for the info being captured from your website. |
The catalog helps build a customer’s profile and user affinity. |
|
Collect Tracking Code
|
A one-pixel JavaScript snippet used to capture data about user behavior on your website. |
Data captured from your website, must be tied to a catalog. |
|
ContactID
|
A unique identifier assigned to a customer to identify them from an anonymous user. |
This ID can never change, so we recommend using a unique key that can be shared between Marketing Cloud Engagement and your website. |
|
Scenarios
|
Algorithms built to provide personalized recommendations. |
These are highly tested, prebuilt algorithms (a set of instructions that get smarter over time) that help drive recommendations. |
|
SKU/Unique ID
|
Each item in your catalog must have a unique_id (also known as an SKU for products). This catalog field must be unique. |
It’s important that the value in your catalog can be referenced by your website so that you can correctly track when a user carts or purchases a unique item. |
|
Product Code/Item
|
A product code (or “item”) refers to a class of related unique products. |
All size variations of an item on a website share the same product code, for example. Each variation has its own SKU or Unique ID. |
How the Magic Works
The trick to all of this is a combination of smart algorithms that are automatically refreshed and fine-tuned for real-time results. This helps you create highly personalized (read: effective) messages. Like most artificial intelligence, Einstein Recommendations love data—and lots of it. To get to that data, a tracking code is added to your website to collect customer info from their browser behavior, preferences, ecommerce history, and more.
Then you, the magical marketer, map the data (also known as metadata) collected from your website to your company’s product content, in the form of a catalog. Finally, you create content templates for those recommendations.
Clear as a crystal ball? If not, no worries. Let’s walk through a scenario. Our favorite hiking and camping retailer, Northern Trail Outfitters (NTO), wants to use Einstein Recommendations. First, the NTO team installs the code on the retailer’s website pages. They then sync a catalog that includes a product photo and the info they want to track about NTO’s products.
When NTO’s loyal customer Rachel Rodriguez logs in to its website and looks at a pair of hiking shoes, Einstein records this info and starts to build Rachel’s customer profile with her viewing history.
When NTO is ready to send an email to Rachel, an email recommendation content block is added before it’s sent.
When Rachel opens her email, she magically sees three different recommended hiking shoes, including the pair she looked at 2 hours ago. One of the new recommended pairs catches her eye, so she clicks Buy Now.
Rachel is happy. Northern Trail Outfitters is happy.
In the next unit, we cover more on implementing Einstein Recommendations for your business.
Resources
- Salesforce Help: Einstein Recommendation Scenarios
- Salesforce Help: Einstein Email Recommendation
- Salesforce Help: Einstein Web Recommendations